Abstract: The present disclosure discloses a single-target tracking method based on a credit allocation network. The credit allocation network is at first provided, which can be updated online using a guiding focus loss function. The credit allocation network generates credit scores for the tracking results by learning features of the target object, which ensures that the reliable samples are updated for storage in the memory pool. In order to better adapt to the changes in the appearance of the target during the tracking process, a new memory selection strategy is provided to collect high-quality tracking results as the memory samples during the tracking process, which further improves the reliability and adaptability of the memory pool. The present disclosure uses the guiding focus loss function to update the credit allocation network online, which can select more reliable memory samples for the memory pool and improve the robustness of the tracking results.

Abstract: A blockchain-based privacy protection method for a CCN includes: executing, by a trusted AAC, an initialization algorithm to generate common parameters and a master key, generating a public key and a private key for each consumer and publisher, and randomly generating, by the trusted AAC, its own public key and private key; calculating a public key, and generating ciphertext and uploading the ciphertext to a CSP; performing transaction on-chaining; and during decryption, finding, by the consumer, transaction information of the content on the consortium blockchain, sending an interest packet based on the transaction information, and obtaining ciphertext CT through a storage address in the transaction information; generating, by the consortium blockchain, an access transaction based on access information of the consumer; sending the ciphertext CT to the consumer through a data packet; and locally decrypting, by the consumer, the ciphertext CT, and verifying correctness of the content.

Abstract: The present application relates to the field of XRF spectra analysis, and discloses a background reduction method for soil XRF spectra based on the XRF-EGAN, which is based on the design mode of GAN model, includes constructing a generator of the model by using a one-dimensional fully convolutional network layer and a residual connection, constructing a discriminator of the model by using one-dimensional convolution and a fully connected layer, and training the XRF-EGAN model by using an adversarial training mode, and then obtaining the trained generator and discriminator, and the generator is a soil XRF background reduction model, which in turn improves the correlation between the net peak area and the content of element of soil XRF, and thus enhances the accuracy of quantitative analysis of element based on XRF spectra.

Abstract: A blockchain-based privacy protection method for a CCN includes: executing, by a trusted AAC, an initialization algorithm to generate common parameters and a master key, generating a public key and a private key for each consumer and publisher, and randomly generating, by the trusted AAC, its own public key and private key; calculating a public key, and generating ciphertext and uploading the ciphertext to a CSP; performing transaction on-chaining; and during decryption, finding, by the consumer, transaction information of the content on the consortium blockchain, sending an interest packet based on the transaction information, and obtaining ciphertext CT through a storage address in the transaction information; generating, by the consortium blockchain, an access transaction based on access information of the consumer; sending the ciphertext CT to the consumer through a data packet; and locally decrypting, by the consumer, the ciphertext CT, and verifying correctness of the content.